Decoupled Smoothing in Probabilistic Soft Logic
Decoupled Smoothing in Probabilistic Soft Logic
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yatong Chen
中科院分区：
文献类型：
--
作者：
Yatong Chen
网络中的节点分类是一个常见的图挖掘任务。在本文中，我们研究如何分离身份（一个节点的属性）和偏好（一个节点喜欢链接的身份的种类）是有用的节点分类在社交网络。在Chin等人（2019）最近的工作的基础上，通过一种名为“身份解耦平滑”的技术来实现身份和偏好的分离，我们展示了表征身份和偏好的模型如何能够捕获网络中的底层结构，从而提高节点分类任务的性能。具体来说，我们使用概率软逻辑（PSL）[2]，一种灵活的声明性统计推理框架，来建模身份和偏好。我们将我们的方法与PSL中实现的原始解耦平滑方法和其他节点分类方法进行了比较，并表明我们的方法在现实世界Facebook数据集的几个评估指标上优于最先进的解耦平滑方法以及其他节点分类方法[24]。
Node classification in networks is a common graph mining task. In this paper, we examine how separating identity (a node’s attribute) and preference (the kind of identities to which a node prefers to link) is useful for node classification in social networks. Building upon recent work by Chin et al. (2019), where the separation of identity and preference is accomplished through a technique called łdecoupled smoothingž, we show how models that characterize both identity and preference are able to capture the underlying structure in a network, leading to improved performance in node classification tasks. Specifically, we use probabilistic soft logic (PSL) [2], a flexible and declarative statistical reasoning framework, to model identity and preference. We compare our approach with the original de-coupled smoothing method and other node classification methods implemented in PSL, and show that our approach outperforms the state-of-the-art decoupled smoothing method as well as the other node classification methods across several evaluation metrics on a real-world Facebook dataset [24].